Altered brain dynamic in major depressive disorder: state and trait features

Temporal neural synchrony disruption can be linked to a variety of symptoms of major depressive disorder (MDD), including mood rigidity and the inability to break the cycle of negative emotion or attention biases. This might imply that altered dynamic neural synchrony may play a role in the persistence and exacerbation of MDD symptoms. Our study aimed to investigate the changes in whole-brain dynamic patterns of the brain functional connectivity and activity related to depression using the hidden Markov model (HMM) on resting-state functional magnetic resonance imaging (rs-fMRI) data. We compared the patterns of brain functional dynamics in a large sample of 314 patients with MDD (65.9% female; age (mean ± standard deviation): 35.9 ± 13.4) and 498 healthy controls (59.4% female; age: 34.0 ± 12.8). The HMM model was used to explain variations in rs-fMRI functional connectivity and averaged functional activity across the whole-brain by using a set of six unique recurring states. This study compared the proportion of time spent in each state and the average duration of visits to each state to assess stability between different groups. Compared to healthy controls, patients with MDD showed significantly higher proportional time spent and temporal stability in a state characterized by weak functional connectivity within and between all brain networks and relatively strong averaged functional activity of regions located in the somatosensory motor (SMN), salience (SN), and dorsal attention (DAN) networks. Both proportional time spent and temporal stability of this brain state was significantly associated with depression severity. Healthy controls, in contrast to the MDD group, showed proportional time spent and temporal stability in a state with relatively strong functional connectivity within and between all brain networks but weak averaged functional activity across the whole brain. These findings suggest that disrupted brain functional synchrony across time is present in MDD and associated with current depression severity.


Neuroimaging data processing
For processing anatomical images, we followed the fsl-anat pipeline using the FMRIB Software Library (FSL) version 6.0 (www.fmrib.ox.ax.uk/fsl) 1 . In the first step, we reoriented the anatomical images to be comparable with the standard template (MNI152) and removed the neck and lower head. Whilst, we corrected images for spatial intensity variation including bias field and radio frequency inhomogeneity. For registering images in standard space (MNI152), the FMRIB's nonlinear image registration tool (FNIRT) with 12 degrees of freedom and a warp resolution of 10 mm was applied. Before tissue segmentation (CSF, GM, and WM), the non-brain tissues were extracted.
For preprocessing resting-state functional images, the high-pass filter of 0.01 Hz and for spatial smoothing a Gaussian kernel of full-width-at-half-maximum (FWHM) of 5 mm was applied and the first five volumes were discarded. Then, functional images were registered to the corresponding anatomical processed images using FMRIB's Linear Image Registration Tool (FLIRT) 2,3 and subsequently to MNI152 standard space with FMRIB's Nonlinear Image Registration Tool (FNIRT). Then, spatial independent components (IC) associated with the time course of rs-fMRI images were obtained with automatic estimation of the number of components using Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) at the subject level. To remove artifactual components, FMRIB's ICAbased X-noisefier (FIX) from the FMRIB Software Library (FSL) was used 4,5 . We did not use global signal regression.

Transition probability maps
The transitions are non-random and moving between specific states is easier than between others. Therefore, we applied a threshold (probability > 0.2) to show only the smoother trajectories. The hidden Markov model was applied to the whole population regardless of the diagnosis (both MDD patients and healthy controls). This approach facilitates comparing the temporal features in common spatial states. Here, other than calculating the transition probability over the population, we also subset the data based to observe if different groups have different transition paths. Since the HMM model is at the population level, applying the statistical test to compare the transition probability maps is not possible (Supplementary Figure 5 and heatmap of transition probability of the entire data: Supplementary Figure 9).

Symptom clustering
For exploring the association of the fractional occupancy (FO) of the states and the self-rated depression cluster of symptoms, we used structured factor analyses with the varimax rotation to find the main factors of symptoms based on the Beck depression inventory II (BDI). Then, the pairwise Pearson's correlation between the factors (eigenvalues) and fractional occupancy (FO) and average life time (ALT) of the states was calculated.

Transition probability in healthy, asymptomatic and symptomatic groups
Qualitatively observing the results regarding the transition probability indicated that a subset of patients with clinically relevant levels of depressive symptoms has a slightly different trajectory of moving between states than asymptomatic patients and healthy groups. We observed that transitioning towards states #1 and then #6 (FO of states #1 and #6: MDD > HC) is more probable for symptomatic patients than for asymptomatic patients and healthy controls. Whereas, it is more probable for healthy participants to move from state #1 to state #5 compared to both asymptomatic and symptomatic patients. However, this trajectory is less probable for asymptomatic patients. This observation highlights that asymptomatic patients might not have similar brain dynamic patterns to healthy controls or symptomatic patients.

Symptom clusters and FO and ALT
We identified three main factors (loading > 0.5). These factors included Negative selfview (body image change, sense of failure, guilt, self-dislike, self-accusation); Social and cognitive symptoms (indecisiveness, work difficulty, fatigability, selfdissatisfaction), and Negative affect (sadness, crying, pessimism). None of the factors showed a significant association with FO and ALT (p > 0.05), and the R-values ranged between -0.2 and 0.2 ( Supplementary Figures 9 and 10).    We compared the FO of six states between participants with one episode, two episodes and three or more episodes of MDD and HC adjusted for age and gender using a linear model.  A) Healthy controls: Once the HC group moved from state #6 to state #1, they prefer to move to state #5 directly. B) asymptomatic patients have a similar transition map to healthy individuals. However, moving from state #1 to state #5 similar to the HC group is less probable for this group. C) Depression status: It is more probable for symptomatic patients to move from state #4 to state #1 directly.